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This paper presents a study on resource control for autoscaling virtual radio access networks (RAN slices) in next-generation wireless networks. The dynamic instantiation and termination of on-demand RAN slices require efficient autoscaling of computational resources at the edge. Autoscaling involves vertical scaling (VS) and horizontal scaling (HS) to adapt resource allocation based on demand variations. However, the strict processing time requirements for RAN slices pose challenges when instantiating new containers. To address this issue, we propose removing resource limits from slice configuration and leveraging the decision-making capabilities of a centralized slicing controller. We introduce a resource control agent (RC) that determines resource limits as the number of computing resources packed into containers, aiming to minimize deployment costs while maintaining processing time below a threshold. The RAN slicing workload is modeled using the Low-Density Parity Check (LDPC) decoding algorithm, known for its stochastic demands. We formulate the problem as a variant of the stochastic bin packing problem (SBPP) to satisfy the random variations in radio workload. By employing chance-constrained programming, we approach the SBPP resource control (S-RC) problem. Our numerical evaluation demonstrates that S-RC maintains the processing time requirement with a higher probability compared to configuring RAN slices with predefined limits, although it introduces a 45% overall average cost overhead. 

To deliver scalable performance to large-scale scientific and data analytic applications, HPC cluster architectures adopt the distributed-memory model. The performance and scalability of parallel applications on such systems are limited by the communication cost across compute nodes. Therefore, projecting the minimum communication cost and maximum scalability of the user applications plays a critical role in assessing the benefits of porting these applications to HPC clusters as well as developing efficient distributed-memory implementations. Unfortunately, this task is extremely challenging for end users, as it requires comprehensive knowledge of the target application and hardware architecture and demands significant effort and time for manual system analysis. To streamline the process of porting user applications to HPC clusters, this paper presents CommAnalyzer, an automated framework for estimating the communication cost on distributed-memory models from sequential code. CommAnalyzer uses novel dynamic program analyses and graph algorithms to capture the inherent flow of program values (information) in sequential code to estimate the communication when this code is ported to HPC clusters. Therefore, CommAnalyzer makes it possible to project the efficiency/scalability upper-bound (i.e., Roofline) of the effective distributed-memory implementation before even developing one. The experiments with real-world, regular and irregular HPC applications demonstrate the utility of CommAnalyzer in estimating the minimum communication of sequential applications on HPC clusters. In addition, the optimized MPI+X implementations achieve more than 92% of the efficiency upper-bound across the different workloads.